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Neutron radiography has been widely employed in nondestructive investigations. The noise and blur that are inevitably generated in the process of neutron radiography seriously decrease the image quality and lead to the loss of image information. In particular, white spot noise, which has the characteristics of nonuniform distribution, large size, and high magnitude, is difficult to remove. To solve this issue, we apply a generative adversarial network-based to convert a degraded neutron image into a clean image. The basic idea is to integrate attention mechanisms into our generation and discrimination networks. A fusion block with a visual attention mechanism is proposed, which can extract more potential features from the images and preserve the image texture as much as possible. In addition, due to the lack of neutron image datasets, we propose a neutron image degradation model to simulate the noise and blur in real neutron images. The results of the experiments show that our method can effectively eliminate noise and blur from neutron images while retaining texture information well. • Proposed a deep learning-based method to remove hybrid noise and blur from neutron images. • A fusion block combines a dense residual block and a spatial channel attention mechanism. • Perceptual loss is used to make our network efficiently learn the high-level information of the image. • Visual evaluation and quantitative analysis illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract A new structure of the Penning ion gauge (PIG) ion source used for neutron tube is designed, and the experiments on beam current extraction of the PIG negative hydrogen (H −) ion source are performed. The influences of the magnetic field of the ion source, different cathode materials and the aperture size of the extraction cathode on the negative hydrogen ion flow are studied. The neutron experiments are performed with a PIG negative hydrogen (H − PIG) ion source D–D neutron tube, and the experimental data demonstrate that this sort of negative hydrogen ion source can be used to produce neutron tubes with good performance. Highlights • A new structure of PIG ion source used for neutron tube is proposed. • Beam current extraction experiments of PIG H − ion source are performed. • The influence of several factors on negative hydrogen ions flow are studied. • The neutron experiments are performed with a PIG H − ion source D–D neutron tube. • This kind H − ion source can be used to produce neutron tubes with good performance. [ABSTRACT FROM AUTHOR]

The local binary pattern with H function (H-LBP) technique enables fast and efficient edge extraction in digital radiography. In this paper, we reformulate the model of H-LBP and propose a novel sparsity-based shrinkage approach, in which the threshold can be adapted to the data sparsity. Using this model, we upgrade fast H-LBP framework and apply it to real digital radiography. The experiments show that the method improved using the new shrinkage approach can avoid elaborately artificial modulation of parameters and possess greater robustness in edge extraction compared with the other current methods without increasing processing time. [ABSTRACT FROM AUTHOR]

As the traditional reactor and accelerator neutron sources are immovable, the research on the compact neutron radiography becomes more and more important. In this study, a portable neutron tube is employed for the compact thermal neutron radiography system. To realize ideal image quality, the optimal material selection and geometry design (viz. the diameter of the aperture, the shape of the channel and the lining material) of the proposed collimator are thereafter carried out by the SuperMC software. Simulation results show that the normalized thermal neutron flux of 1. 42 × 1 0 − 6 ncm − 2 s − 1 can be successfully realized at the exit of the collimator by the proposed collimator with a satisfied nonuniformity of 4.6%. • The neutron tube is used as the source of the compact neutron radiography system. • The proposed collimator is simulated by the SuperMC software. • 45# transformer oil is used for heat dissipation and insulation. • Graphite and beryllium are selected as the reflector materials. • The shape of collimator is cylindrical-conical. [ABSTRACT FROM AUTHOR]

Neutron radiography (NR) has been successfully implemented on reactors and accelerators in the past few decades. However, the promotion of NR is severely hindered by the immovability of the reactor neutron source. In order to solve this problem, the advanced neutron tube is employed as the neutron source of the compact NR system. To maintain the thermal neutron flux and reduce the gamma dose rate at the image plane, we give a novel design of moderator and collimator for the compact NR systems by using SuperMC software. Simulation results show that, under the neutron source yield of 1 × 1 0 9 n ∕ s , we can obtain a uniform thermal neutron flux of 1. 03 × 1 0 − 5 cm − 2 per source particle with n/ γ ratio of 2. 47 × 1 0 11 n cm − 2 Sv − 1 at the sample position by using the proposed moderator and collimator device, thus demonstrating its application potential in the compact NR systems. [ABSTRACT FROM AUTHOR]

Neutron radiography (NR) has been widely used in non-destructive investigations. Since the industrial neutron radiographic images (NRIs) usually suffer from unclear defect features, insufficient data volume, and low efficiency of manual detection, we propose a proof-of-concept defect-detection method based on the modified YOLO network for the degraded NRIs in this paper. Firstly, 6336 radiographic images including crack, inclusion, blow hole, and residual core are built as the defect-detection dataset. Secondly, the types and the relative coordinates of defects are labeled by a graphical image annotation tool (i.e., Labelimg). Finally, the adaptive spatial feature fusion (ASFF) and convolutional block attention module (CBAM) are introduced to the modified YOLO network to enhance its ability of small-size defect detection. Experimental results demonstrate that the proposed method can achieve the average accuracy of 98.1% and detection rate of 85.985 frames per second (FPS) with a single NVIDIA GeForce RTX 4090 GPU on the built radiographic image dataset. In addition, the proposed method shows a good potential in detecting the above defects contained in the real NRIs. • A YOLO network-based automatic defect detection method with higher accuracy and lower detection time is proposed. • Data augmentation is employed to build a large-scale radiographic-image dataset with 6336 defect images. • ASFF and CBAM are used to solve the inconsistency of different-scale features and enhance the small-size detection ability. [ABSTRACT FROM AUTHOR]

Objective image quality metric is a challenging problem for neutron radiography due to the lack of reference images. The existing no-reference image quality metric (NR-IQM) method designed for neutron radiographic images (NRIs) still exists two main shortcomings. Firstly, the limited L/D of the collimator would inevitably lead to geometric blur in the NRIs. However, the special distortion type of mixed Poisson–Gaussian noise is merely considered but ignores the blur distortion. Secondly, the distortion levels of Poisson noise added to an image cannot be adjusted. In order to address these two issues, a more practical objective NR-IQM method is proposed to predict the perceptual quality of NRIs in this paper. Experimental results demonstrate that the proposed method shows good consistency with the quality of authentic NRIs and also performs well in reflecting the tiny quality difference of enhanced NRIs, thus demonstrating its application potential in neutron radiography. [ABSTRACT FROM AUTHOR]

In this paper, a neutron source system for compact thermal neutron radiography (TNR) was designed and optimized to improve its moderation efficiency via SuperMC simulations. The DSNH long-life D–T neutron tube with self-target technology independently developed by Northeast Normal University is used as the neutron source. The main optimizations in material and structure include the use of tungsten and lead primary moderators, polyethylene secondary moderators, sapphire filter, and 4 cm magnesium thermal neutron aggregation layer. The improved system provides thermal neutron flux of 3. 77 × 1 0 4 n cm − 2 s − 1 with thermal neutron content of 85.20% at the exit of the neutron channel, which is suitable for thermal neutron radiography. [ABSTRACT FROM AUTHOR]

Neutron imaging (NI) has been widely employed in non-destructive investigations. Since the image quality assessment (IQA) method can be beneficial in reflecting the performance of imaging systems and image processing algorithms, we propose a proof-of-concept IQA method for the NI system based on a deep bilinear convolutional neural network (CNN) framework with two designed datasets. Due to the lack of neutron IQA database, different levels of authentic distortion induced by NI are first simulated on the natural and neutron radiographic images to generate the pre-training and fine-tuning datasets, respectively. Then, the gradient magnitude similarity deviation (GMSD) algorithm and transfer learning method are respectively employed to label the above datasets and optimize the prediction performance. Experimental results demonstrate that the proposed method can maintain good consistency with human perception in predicting the quality scores of both the authentic and enhanced neutron radiographic images. [ABSTRACT FROM AUTHOR]